Intelligent Control Design for Hybrid Maglev Transportation System

• Main Authors: Jing-Xiang Yao, 姚景翔
• Other Authors: Rong-Jong Wai
• Format: Others
• Language: en_US
• Online Access: http://ndltd.ncl.edu.tw/handle/533cgx

碩士 === 元智大學 === 電機工程學系 === 102 === In general, a maglev transportation system contains two parts including magnetic levitation and propulsion mechanism. The subject of inherently unstable electromagnetic force and nonlinear attitude control of the moving platform in the maglev mechanism is one of interesting research topics at present. On the other hand, the corresponding control performance of the propulsion mechanism is influenced easily by the normal force produced by the hybrid maglev mechanism so that the coupled dynamic model of the hybrid maglev transportation system is highly nonlinear and time varying. Therefore, this thesis designs and analyzes the dynamic model for a hybrid maglev transportation system. The suspension power loss can be reduced broadly by introducing a permanent magnet (PM) to the electromagnet. The reason is that the PM provides a primary magnetic force to balance the weight of the magnet and the carrier, while the electromagnet is excited for adjusting the air gap to an expected value. Then, a backstepping control (BSC) is firstly designed for the stable balancing and tracking control of a hybrid maglev transportation system. In order to relax the requirement of detailed system parameters, to eliminate the chattering phenomena, and to deal with the explosion terms caused by repeated differentiations in backstepping design procedure and the possible problem of actuator saturations in conventional backstepping control, a backstepping fuzzy-neural-network control (BFNNC) scheme is further investigated. In the proposed BFNNC scheme, a FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. Most of control schemes require the measurement of joint positions and velocities or utilizes the differential method via only position measurements to obtain the information of velocities. While accurate position sensors are readily available, velocity sensors may not be accessible because of noise contamination and installation cost. Thus, an adaptive observer is designed in this thesis to estimate the velocity signals for the later control utilization, and an adaptive observer and control (AOC) scheme is formed by means of the stability analyses of the entire system. The ultimate goal is to design an on-line fuzzy-neural-network (FNN) velocity-sensorless control methodology to cope with the problem of the complicated control transformation and the requirement of detailed system parameters in the AOC scheme, and to directly ensure the stability of the entire system without the requirement of strict constraints, detailed system information and auxiliary compensated controllers despite the existence of uncertainties. The effectiveness and robustness of the proposed control strategies in this thesis are verified by numerical simulations and experimental results.